import sys
import os
import logging
import re
import json
from typing import Dict, List, Any, Optional, Tuple

# 添加项目根路径到 sys.path
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..'))

# 导入 MySQLUtil
from shared.utils.MySQLUtil import MySQLUtil

# 设置日志
logger = logging.getLogger(__name__)

def get_专业对口程度的评价分布(
    project_id: int,
    questionnaire_ids: List[int],
    product_code: Optional[str] = None,
    project_code: Optional[str] = None,
    region_code: Optional[str] = None,
    education: Optional[str] = None
) -> Dict[str, Any]:
    """
    专业对口程度的评价分布 - 指标计算函数
    
    ## 指标说明
    该指标用于评估毕业生工作与所学专业的对口程度分布情况。通过统计问卷中选择"很对口"、"对口"和"基本对口"三个选项的人数占总人数的比例，
    反映毕业生的专业对口程度。计算结果可用于分析毕业生就业质量。
    
    ## Args
        project_id (int): 项目ID，用于查询项目配置信息
        questionnaire_ids (List[int]): 问卷ID集合，用于确定数据范围
        product_code (Optional[str]): 产品编码，用于路由到特定计算逻辑
        project_code (Optional[str]): 项目编码，用于路由到特定计算逻辑
        region_code (Optional[str]): 区域编码，用于路由到特定计算逻辑
        education (Optional[str]): 学历筛选条件，可选值为[本科毕业生，专科毕业生，硕士研究生，博士研究生]
        
    ## 示例
    ### 输入
    ```json
    {
        "project_id": 5895,
        "questionnaire_ids": [11158, 11159]
    }
    ```
    
    ### 输出
    ```json
    {
        "success": true,
        "message": "ok", 
        "code": 0,
        "result": {
            "summary": {
                "professional_match_ratio": 0.8571,
                "total_count": 525,
                "match_count": 450,
                "description": "专业对口程度为 85.71%"
            },
            "distribution": [
                {
                    "key": "1",
                    "val": "很对口",
                    "count": 100,
                    "ratio": 0.1905,
                    "percentage": "19.05%"
                },
                {
                    "key": "2",
                    "val": "对口",
                    "count": 150,
                    "ratio": 0.2857,
                    "percentage": "28.57%"
                },
                {
                    "key": "3",
                    "val": "基本对口",
                    "count": 200,
                    "ratio": 0.3810,
                    "percentage": "38.10%"
                },
                {
                    "key": "4",
                    "val": "不对口",
                    "count": 50,
                    "ratio": 0.0952,
                    "percentage": "9.52%"
                },
                {
                    "key": "5",
                    "val": "很不对口",
                    "count": 25,
                    "ratio": 0.0476,
                    "percentage": "4.76%"
                }
            ]
        }
    }
    ```
    """
    logger.info(f"开始计算指标: 专业对口程度的评价分布, 项目ID: {project_id}")
    
    try:
        db = MySQLUtil()  

        # 1. 查询项目配置信息
        project_sql = """
        SELECT client_code, item_year, dy_target_items, split_tb_paper 
        FROM client_item 
        WHERE id = %s
        """
        project_info = db.fetchone(project_sql, (project_id,))
        if not project_info:
            raise ValueError(f"未找到项目ID={project_id}的配置信息")

        client_code = project_info['client_code']
        item_year = project_info['item_year']
        split_tb_paper = project_info['split_tb_paper']
        
        logger.info(f"项目配置: client_code={client_code}, item_year={item_year}, split_tb_paper={split_tb_paper}")

        # 2. 计算 shard_tb_key
        shard_tb_key = re.sub(r'^[A-Za-z]*0*', '', client_code)
        logger.info(f"计算得到 shard_tb_key: {shard_tb_key}")

        # 3. 查询问卷信息
        questionnaire_sql = f"""
        SELECT id, dy_target 
        FROM wt_template_customer 
        WHERE id IN ({','.join(['%s'] * len(questionnaire_ids))})
        """
        questionnaires = db.fetchall(questionnaire_sql, tuple(questionnaire_ids))
        if not questionnaires:
            raise ValueError(f"未找到问卷ID集合={questionnaire_ids}的配置信息")
        
        logger.info(f"查询到问卷信息: {questionnaires}")

        # 4. 过滤特定调研对象的问卷
        valid_questionnaire_ids = [q['id'] for q in questionnaires if q['dy_target'] == 'GRADUATE_SHORT']
        if not valid_questionnaire_ids:
            raise ValueError("未找到目标调研对象的问卷ID")
            
        logger.info(f"找到有效问卷ID: {valid_questionnaire_ids}")

        # 5. 查询问题信息
        question_sql = """
        SELECT id, wt_code, wt_obj 
        FROM wt_template_question_customer 
        WHERE cd_template_id = %s AND wt_code = 'T00000651' AND is_del = 0
        """
        question_info = db.fetchone(question_sql, (valid_questionnaire_ids[0],))
        if not question_info:
            raise ValueError("未找到指定问题编码的问题信息")
            
        logger.info(f"找到问题信息: {question_info['id']}")

        # 6. 解析问题选项
        wt_obj = json.loads(question_info['wt_obj'])
        options = []
        for item in wt_obj['itemList']:
            options.append({
                'key': item['key'],
                'val': item['val'],
                'weight': item.get('weight', 1)
            })

        # 7. 构建动态表名
        answer_table = f"re_dy_paper_answer_{split_tb_paper}"
        student_table = f"dim_client_target_baseinfo_student_{item_year}"

        # 8. 构建SQL查询
        where_clause = ""
        if education:
            where_clause = f"AND s.education = '{education}'"
            
        sql = f"""
        SELECT
            SUM(t1.c1) as c1,
            SUM(t1.c2) as c2,
            SUM(t1.c3) as c3,
            SUM(t1.c4) as c4,
            SUM(t1.c5) as c5
        FROM
            {answer_table} t1
        JOIN 
            {student_table} s ON t1.target_no = s.target_no
        WHERE
            t1.cd_template_id = %s
            AND t1.wid = %s
            AND t1.ans_true = 1
            AND s.shard_tb_key = %s
            AND s.item_year = %s
            {where_clause}
        """
        params = (valid_questionnaire_ids[0], question_info['id'], shard_tb_key, item_year)
        result = db.fetchone(sql, params)
        if not result:
            raise ValueError("未找到有效的答案数据")
        
        # 计算前三项占比和各选项统计
        counts = [result[f'c{i}'] or 0 for i in range(1, 6)]
        total = sum(counts)
        
        if total == 0:
            ratio = 0
            options_data = []
        else:
            ratio = (counts[0] + counts[1] + counts[2]) / total
            options_data = []
            
        # 构建选项数据，包含百分比
        option_names = ["很对口", "对口", "基本对口", "不对口", "很不对口"]
        for i, (count, name) in enumerate(zip(counts, option_names)):
            percentage = (count / total * 100) if total > 0 else 0
            options_data.append({
                "key": str(i + 1),
                "val": name,
                "count": count,
                "ratio": round(count / total, 4) if total > 0 else 0,
                "percentage": f"{percentage:.2f}%"
            })

        logger.info(f"总计 {total} 人，专业对口程度（前三项）比例: {ratio:.4f}")
        logger.info(f"各选项分布: {[(opt['val'], opt['count'], opt['percentage']) for opt in options_data]}")
        
        logger.info(f"指标 '专业对口程度的评价分布' 计算成功")
        return {
            "success": True,
            "message": "ok",
            "code": 0,
            "result": {
                "summary": {
                    "professional_match_ratio": round(ratio, 4),  # 专业对口程度比例：前三项（很对口、对口、基本对口）占总数的比例
                    "total_count": total,
                    "match_count": sum(counts[:3]),  # 前三项总数
                    "description": f"专业对口程度为 {ratio * 100:.2f}%"
                },
                "distribution": options_data
            }
        }

    except Exception as e:
        logger.error(f"计算指标 '专业对口程度的评价分布' 时发生错误: {str(e)}", exc_info=True)
        return {
            "success": False,
            "message": f"数据获取失败: 专业对口程度的评价分布",
            "code": 500,
            "error": str(e)
        }